Spatial Autoregressive Error Model Research Articles

Soil water availability strongly drives the differentiation of acquisitive and conservative strategies for coexisting woody species in a Chinese subtropical evergreen forest

AbstractQuestionsTrait–environment relationships hold great promise for elucidating the mechanisms that drive community assembly. We asked to what extent does functional trait coordination underlie the differentiation of acquisitive–conservative strategies along environmental gradients? Also, to what extent does soil water availability determine trait combinations and subsequently shape microhabitat preferences for species with distinct ecological strategies?LocationYunnan Province, southwest China.MethodsPearson correlation and trait network analyses were used to quantify trait associations. A combined RLQ–fourth‐corner analysis and a spatial autoregressive error model were employed to examine trait–environment relationships and explain species distribution along environmental gradients.ResultsLeaf and stem traits were tightly coordinated along the first RLQ axis. Herein, traits representing construction cost exhibited trade‐offs against traits representing resource acquisition rate, underlying an acquisitive–conservative strategy differentiation along the environmental gradients. Significant overall and bivariate trait–environment relationships were found. In addition, the topographic wetness index contributed substantially to the environmental variation, and significantly influenced most traits alone. These results supported the hypothesis that soil water availability was the key environmental factor in selecting trait combinations and driving the differentiation of acquisitive and conservative strategies for coexisting species compared with other environmental factors. Finally, deciduous and a proportion of the evergreen species were acquisitive, whereas the remaining evergreen species were conservative, preferring wetter and drier microhabitats, respectively.ConclusionsOur findings indicate that soil water availability plays a central role in shaping both the trait combinations and microhabitat preferences of species with different ecological strategies rather than different leaf habits. These findings also highlight the fundamental significance of functional traits in facilitating the differentiation between acquisitive and conservative strategies along environmental gradients.

Wages of Workers Spatial Analysis in Indonesia Region 2019

The wage inequality of workers in Indonesia is one of the main problems and concerns that are important to be addressed by the government. The determination of the regional minimum wage by the local government has not been able to solve the problem of inequality. On a larger scale, the wage inequality of workers can affect the stability of the national economy. Research on the spatial analysis of workers' wages is very important to be carried out as a basis for making appropriate policies by the government. In this study, we have succeeded in analyzing the dependence and spatial relationship of a region with the wages of its workers and have identified the factors that affect the wages of workers in a region. The result reveals the spatial dependences are detected among districts, followed by the spatial clusters and spatial outliers through global and local spatial autocorrelation. Applying two spatial autoregressive models, spatial autoregressive lag model (SAL) and spatial autoregressive error model (SEM), SAL confirmed that there are 4 significant independent variables with a level of 10 percent and have a positive relationship, namely education, age, internet, and sex ratio variables. And SEM confirmed that there are significants 5 significant independent variables with a level of 10 percent and have a positive relationship, namely education, age, technology, internet, and sex ratio variables. As the policy implication, since regional inequality in term of wage is still a major issue, it will be a call for better coordination and cooperation within and between regions.

Human Development Dynamics across Districts of Indonesia: A Study of Regional Convergence and Spatial Approach

Indonesia is an archipelago country which comprises of two main parts, western and eastern regions and spread into over 500 districts. Each district has their own characteristics, especially in development aspect. Some districts have been growing faster on economic and social development, yet others still fall behind. Using Human Development Index and its components on education, health and economic variables over 2010- 2018 period, this study aims to examine convergence on the regional human growth process and investigate the speed of convergence across districts. The result reveals convergence occurred on human development process and its determinants across districts during 2010-2018. Education variables are assumed as the main contributor for boosting the speed of convergence of human development. Spatial dependences are detected among districts, followed by the spatial clusters and spatial outliers through global and local spatial autocorrelation. Applying two spatial autoregressive models, spatial autoregressive lag model (SAR) and spatial autoregressive error model (SEM), confirmed that there is significant spatial spill-overs.The speed of convergence for all variables are much declining after the inclusion of spatial lag and error model. As the policy implication, since regional inequality in term of human development is still a major issue, it will be a call for better coordination and cooperation within and between regions.

The impact of agricultural conservation easement on nearby house prices: Incorporating spatial autocorrelation and spatial heterogeneity

The impact of farmland under agricultural conservation easement (ACE) contract on the values of nearby residential properties is investigated using housing sales data in two Pennsylvania counties. ACE-protected farmland had a positive impact on nearby property values in one study county but a negative impact in the other. The paper also looks at the impact of forest land use, and discovers that preserved forest land had a positive impact on the nearby property values in both counties. House prices showed strong spatial correlation in both counties, and a spatial error components (SEC) model fit the data better than the OLS model, a spatial-lag model (SLM), or a spatial autoregressive error model (SEM). Geographically weighted regression (GWR) showed that the impact of ACE-protected farmland on nearby property values varied within one of the two study counties, with positive impacts in some parts of the county and negative impacts in other parts. The impact of forest cover on property values also varied, with positive impacts within both counties. A new hybrid GWR-SEC model is introduced that incorporates both spatial correlation in prices and spatial heterogeneity in the model parameters. Statistical goodness of fit measures showed that the GWR-SEC model fit better than the GWR model or a hybrid GWR-SEM model.

Improved GMM estimation of the spatial autoregressive error model

We suggest an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are different from observable regression residuals.

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.

Designs for Detecting Spatial Dependence

The aim of this article is to find optimal or nearly optimal designs for experiments to detect spatial dependence that might be in the data. The questions to be answered are: how to optimally select predictor values to detect the spatial structure (if it is existent) and how to avoid to spuriously detect spatial dependence if there is no such structure. The starting point of this analysis involves two different linear regression models: (1) an ordinary linear regression model with i.i.d. error terms—the nonspatial case and (2) a regression model with a spatially autocorrelated error term, a so‐called simultaneous spatial autoregressive error model. The procedure can be divided into two main parts: The first is use of an exchange algorithm to find the optimal design for the respective data collection process; for its evaluation an artificial data set was generated and used. The second is estimation of the parameters of the regression model and calculation of Moran's I, which is used as an indicator for spatial dependence in the data set. The method is illustrated by applying it to a well‐known case study in spatial analysis.

SPATIALLY‐VARYING EFFECTS OF REZONING ON HOUSING PRICE

A hedonic price equation is estimated at the parcel level to capture the spatially‐varying effects of rezoning on housing prices using a geographically weighted regression ‐ spatial autoregressive error model (GWR‐SEM). The empirical results highlight a number of points. Firstly, the positive amenities from rezoning to office use act as a buffer and transition the zones between single family housing to commercial zones near downtown areas may be applicable to approving the same kind of rezoning in other areas. Secondly, when approving rezoning from agriculture to residential or commercial use, it may be necessary to anticipate the positive amenities from rezoning in less well‐off neighborhoods and the negative amenities from rezoning in well‐off neighborhoods. Thirdly, the positive amenities from well‐designed open space preservation districts may encourage county planners to consider similar types of projects in the future.

Relationship Between Secondary Variables and Soybean Oil and Protein Concentration

The spatial variation of soybean seed oil and protein concentration has been demonstrated at a field scale. We hypothesize that aerial images of crops during the late growing season, in combination with site property maps, can be used to delineate areas of similar seed oil and protein concentration within a given field. If these areas can be identified, then they can be harvested differentially. The main objective of this is study was to predict soybean yield and seed composition using site properties (elevation, slope, soil electrical conductivity (ECdeep and ECshallow), soil organic matter (SOM), and soil reflectance) as well as indices derived from hyperspectral remote sensing images (NNDVI, PRI, NDVI, GNDVI, and the ratio between NDVI and GNDVI). The study was conducted on two adjacent 16 ha subsections during the 2000 and 2001 growing seasons. Since the predictor variables presented multicollinearity, three groups of principal components (PC) were defined: a group corresponding to the site properties, a group corresponding to vegetation indices, and a group corresponding soybean performance (seed yield, and seed oil and protein concentrations). Then a spatial autoregressive error model predicted the soybean performance PC using site properties and vegetation indices while accounting for spatial the autocorrelation. Relationship between site properties, hyperspectral vegetation indices, and soybean seed protein and oil concentration were found for both environments. The regression analysis indicated that PCs of vegetation indices predicted soybean seed protein and oil concentration more consistently than site properties.

On the property of diffusion in the spatial error model

The aim of this paper is to illustrate the property of global spillover effects in the first-order spatial autoregressive error model and the associated diffusion process of spatial shocks. An application is provided on a sample of 145 regions over 1989–1999 and highlights the most influential regions.